The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics
Author(s) -
Danica Wiredja,
Mehmet Koyutürk,
Mark R. Chance
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx415
Subject(s) - phosphoproteomics , computer science , inference , source code , r package , web application , computational biology , data mining , kinase , world wide web , artificial intelligence , protein kinase a , biology , operating system , programming language , protein phosphorylation , microbiology and biotechnology
Computational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to estimating changes in a kinase's activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to implement the algorithms. Thus, to make it accessible to the larger scientific community, we present a web-based application of this method: the KSEA App. Overall, we expect that this tool will offer a quick and user-friendly way of generating kinase activity estimates from high-throughput phosphoproteomics datasets.
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